Spring 2020

Content

  • Introduction
  • Methods
    • Project overview
    • Data
  • Results
  • Discussion

Introduction

Prediction of protein-protein interactions (PPI) are a challenging task.

ML models allow to exploit the content of these PPI data sets.

The aim of this project is to create a toolbox to predict the biological activity of these peptides with machine learning models.

  • Features:
    • Support for both sequence or variant input.
    • Support for several sequence encoders.
    • Support for several models.
    • Visualization options.

Project overview

Methods - packages used

Function Library
Data loading readxl
Data cleaning and wrangling dplyr , broom (tidyverse)
Data augmenting dplyr (tidyverse),Peptides
Extracting data UniprotR
Plotting ggplot2(tidyverse), ggseqlogo,ggpubr
Analysing stats
Modeling keras,neuralnet, caret, yardstick, glmnet,ANN2

Methods - the data sets

Protein Target Biological activity Species Num of variants Score
Data set 1 BRCA1 BARD1 RING domain Ubiquitin E3 activity H. sapiens 5610 Y2H assays
Data set 2 ERK2 Small molecule (SCH772984) Resistance to drugs H. sapiens 6810 Drug sensitivity assays. Calculation of cell availability
Data set 3 LDLRAP1 OBFC1 Protein translation H. sapiens 6385 Y2H assays
Data set 4 Pab1 el4FG1 Translation initiation S. cereviseae 1340 Y2H assays

Example data set 4 - data overview

Example data set 4 - heatmap

Example data set 4 - conserved regions

Machine learning toolbox

  • Ideas for supported machine learning framework:

    • Gaussian Process Regression.
    • Artificial Neutral Network.
    • ElasticNet Regression.

Results

Discussion

References

  • Data set 1: L. M. Starita, D. L. Young, et al. Massively parallel functional analysis of brca1 ring domainvariants, Genetics, vol. 200, no. 2, pp. 413–422, 2015.
  • Data set 2: L. Brenan, A. Andreev, et al. Phenotypic characterization of a comprehensive set of missense mutants. Cell Reports, vol. 17, no. 4, pp. 1171–1183, 2016.
  • Data set 3: A deep mutational scan of LDLRAP1 based on a Y2H assay with the interactor OBFC1. [https://www.mavedb.org/scoreset/urn:mavedb:00000036-a-1/]
  • Data set 4: D. Melamed, D. L. Young, et al. Combining natural sequence variation withhigh throughput mutational data to reveal protein interaction sites. PLOS Genetics, vol. 11, no. 2,pp. 1–21, 2015.

Appendix

R script overview 1

  • 02_clean.R
    • Load: Load data from 01_load.R
    • Wrangle data: Remove NaN, fixes
    • Save cleansed data in .tsv format
  • 03_augment.R
    • Load data from 02_clean.R
    • Augment data: Calculate sequences, descriptors (pI, MW, Z-Scale)
    • Save augmented data in .tsv format

Appendix

R script overview 2

  • 04_model_i.R
    • Load augmented data
    • Perform model fitting
    • Predict unknowns
    • Plotting and reporting

Appendix

R script overview 3

  • 99_proj_func.R
    • Sequence encoder
    • Sequence generator
    • Plot functions